Advanced Early Disease Prediction and Exploration Service (AEDES) is a unified, modular surveillance platform for vector-borne and zoonotic diseases. Originally developed for dengue forecasting in the Philippines, AEDES has evolved into a flexible framework for monitoring multiple diseases across different geographic contexts using big data, AI, and environmental intelligence. Our mission is to empower public health agencies and non-profits with real-time dashboards and predictive models that identify disease outbreaks before they spread, enabling rapid, targeted interventions.

AEDES was originally created to address dengue crisis in the Philippines—a persistent public health emergency. In 2019, the Philippines reported 146,062 dengue cases and 622 deaths. By 2022, cases surged to 221,000 (182% increase). The AEDES dengue surveillance system successfully demonstrated that combining case data with environmental signals (climate, satellite imagery, search trends) could provide early warnings of outbreaks 2-3 weeks ahead of official case reports.

This proven approach is now being adapted globally.

The United States faces a growing threat from multiple tick-borne and mosquito-borne diseases. Colorado is particularly vulnerable due to its position on major bird migration routes and diverse ecology that supports multiple vector species.

• Cases in Colorado: Rising (2023: 42 reported cases)
• Vector: Ixodes scapularis ticks (black-legged ticks) and I. pacificus (western ticks)
• Peak Season: April-October (highest June-August)
• Risk Areas: Forested and wooded areas, especially eastern and western Colorado
• Trend: Expanding northward and westward as climate warms

• Cases in Colorado: Variable (2023: 8 reported cases)
• Vector: Culex mosquitoes (night-feeding)
• Peak Season: June-October (highest August-September)
• Risk Areas: Urban areas with stagnant water, irrigation systems
• Trend: Persistent endemic circulation

• Cases in Colorado: Low but potentially underreported
• Vector: Dermacentor (American dog tick) and Ixodes ticks
• Peak Season: April-June (spring tick season)
• Trend: Sporadic cases, often misdiagnosed initially

• Wild Bird Detections: Growing across Colorado (H5N1 confirmed)
• Risk to Poultry: High economic impact (commercial flocks)
• Risk to Humans: Occupational exposure for farm workers, wildlife handlers
• Monitoring: Real-time via USGS, eBird observations
• Trend: Seasonal peaks during spring (March-May) and fall (August-October) migration

The Challenge:

• Multiple diseases with overlapping seasons (spring: ticks + bird migration; summer: mosquitoes)
• Cases reported with 1-3 week delays to public health agencies
• Different data sources (CDPHE cases, CPW wildlife data, weather, Google Trends) are not integrated
• Forecasting capability lags by weeks compared to environmental signals

The AEDES Solution:

• Real-time data integration: Combine CDPHE case reports with environmental signals
• Early warning: Use climate patterns, wildlife observations, Google Trends to forecast 2-4 weeks ahead
• Multi-disease monitoring: Track Lyme, WNV, RMSF, and bird flu spillover risk in one unified dashboard
• Risk stratification: Identify high-risk counties and time periods for targeted interventions
• Occupational health: Monitor wildlife handlers and farm workers for disease exposure

AEDES Colorado integrates five data streams for each disease:

1. Case Surveillance (from Colorado Department of Public Health & Environment)

• Confirmed/probable cases by date, location, demographics
• Hospital admissions, severity data
• Deaths and complications

2. Vector & Wildlife Monitoring

• Tick surveillance (iNaturalist observations, citizen science)
• Mosquito trapping data (CPW)
• Dead bird reports (USGS, iNaturalist)
• Occupational exposure incidents

3. Environmental Signals

• Temperature and humidity (NOAA weather)
• Precipitation patterns
• Satellite land cover data (vegetation, water bodies)
• Tick life cycle models based on climate

4. Search & Social Signals

• Google Trends for disease-related searches
• News media mentions
• Social media signals
• Public health alert volume

5. Migration & Phenology Data

• Bird migration intensity (spring/fall peaks)
• Tick emergence timing (based on growing degree days)
• Mosquito breeding habitat conditions

Integration Model:

Real-Time Data → Analysis Engine → Risk Score → Forecast → Dashboard
     ↓              ↓                 ↓           ↓          ↓
  CDPHE cases   Environmental       Tick Risk   Week 1-4   Public Health
  iNaturalist   correlation         WNV Risk    Cases      Officials
  Weather       Machine learning    Bird Flu    Alerts     Clinicians
  Google Trends Predictive models   RMSF Risk   Maps       Community
  USGS          Historical patterns                        

AEDES Colorado provides:

• Real-time case maps by disease
• 4-week risk forecasts with confidence intervals
• Tick activity hotspot predictions
• Mosquito breeding habitat risk assessment
• Bird migration intensity index (early warning for spillover)
• County-level risk scores
• Occupational exposure alerts
• Automated notifications when thresholds exceeded

The AEDES project includes comprehensive automated tests for data collection, processing, and surveillance dashboard generation. Tests cover both refactored Python modules and legacy data extraction implementations.

Install test dependencies:

pip install -e . --no-deps
pip install pytest pytest-cov pytest-mock

Run all tests (excluding optional geo dependencies):

pytest tests/ \
  --ignore=tests/test_geoboundaries.py \
  --ignore=tests/test_nasa_worldview.py \
  --ignore=tests/test_osm.py \
  -v

Run tests with coverage reporting:

pytest tests/ \
  --ignore=tests/test_geoboundaries.py \
  --ignore=tests/test_nasa_worldview.py \
  --ignore=tests/test_osm.py \
  --cov=scripts \
  --cov=src/aedesproject_uif \
  --cov-report=term-missing \
  --cov-report=html

• tests/test_demographics.py – Refactored aedesproject_uif.data_extraction.demographics module
• tests/test_google_trends.py – Google Trends data extraction and saving
• tests/test_meteorological.py – Legacy meteorological data fetching (via tests.src)
• tests/test_nasa_appeears.py – Legacy NASA APPEEARS API integration
• tests/test_scripts.py – Surveillance data pipeline (scripts/fetch_surveillance_data.py, scripts/generate_dashboard.py)
• tests/conftest.py – Shared pytest fixtures and test utilities

Optional Tests (require additional dependencies):

• tests/test_geoboundaries.py – Admin boundaries (requires geopandas + fiona)
• tests/test_nasa_worldview.py – NASA Worldview data (requires wget)
• tests/test_osm.py – OpenStreetMap queries (requires geopandas + osmnx)

Tests run automatically on every push and pull request to main via .github/workflows/test-coverage.yml using Python 3.11. Coverage reports are uploaded to Codecov.

For detailed information on how to use and leverage the full potential of aedesproject-uif please refer to the documentation available at:

https://cirrolytix.github.io/aedesproject-uif/ 📚

Project AEDES uses the following open licenses:

• MIT License
• Creative Commons Attribution Share Alike 4.0 International

Project AEDES is in active development and continously maintained by Cirrolytix Research Services.

• Dominic Ligot, Founder and Chief Executive Officer
• Emily Jo Vizmonte, Analytics Consultant
• Claire Tayco, Managing Consultant and Chief Data Scientist
• Cricket Soong, Chief of Operations

Whether you're here to contribute to our Python package or to collaborate and use our web-based risk portal, we are excited to have you on board.

We are always on the lookout for enthusiastic developers to help us improve our Python package. Whether you are fixing bugs, adding new features, or improving documentation, your contributions are highly valued.

• Getting Started: Check out our Contributing Guidelines to understand how you can start contributing.
• Code of Conduct: We believe in fostering an inclusive and welcoming environment. Please read our Code of Conduct to understand our community's values and expectations.

If you are here to use our risk portal and explore the visualizations, welcome! Our app is designed to provide actionable insights and we hope it serves your needs.

• Getting Started: Navigate to the app's main page and explore the various features and visualizations available.
• Feedback: Your feedback is invaluable. If you have suggestions, improvements, or find any issues, please raise them in the issues section.
• Code of Conduct: We aim to create a positive experience for all our users. Familiarize yourself with our Code of Conduct to understand the community's expectations.

If you have other questions or feedback, contact us via email at [email protected].

Follow our social media channels to stay updated with the latest information, news, and community discussions.

Twitter: @aedes_ai Facebook: @aedesproject.org LinkedIn: company/project-aedes

If you use this software, please cite it using the following metadata.

Ligot, D.V., Tayco, F.C., Soong, G.K., and Vizmonte, E.J.. aedesproject-uif (Version 4.0.0) [Computer software]

@software{aedesproject-uif,
author = {Ligot, Dominic Vincent, Tayco, Frances Claire, Soong, Gabriel Kristopher, and Vizmonte, Emily Jo},
license = {MIT License},
month = {9},
title = {{aedesproject-uif}},
version = {4.0.0},
year = {2023}
}

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